The Internet-of-Things provides us with lots of sensor data. However, the data by themselves do not provide value unless we can turn them into actionable, contextualized information. Big data and data visualization techniques allow us to gain new insights by batch-processing and off-line analysis. Real-time sensor data analysis and decision-making is often done manually but to make it scalable, it is preferably automated. Artificial Intelligence provides us the framework and tools to go beyond trivial real-time decision and automation use cases for IoT.
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
The Internet-of-Things provides us with lots of sensor data. However, the data by themselves do not provide value unless we can turn them into actionable, contextualized information. Big data and data visualization techniques allow us to gain new insights by batch-processing and off-line analysis. Real-time sensor data analysis and decision-making is often done manually but to make it scalable, it is preferably automated. Artificial Intelligence provides us the framework and tools to go beyond trivial real-time decision and automation use cases for IoT.
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
AIoT(Artificial intelligence of things) is a relatively new term and has recently become a hot topic which combines two of the hottest acronyms AI( Artificial intelligence) and Internet of things (IoT)
An introduction to AI (artificial intelligence)Bellaj Badr
An introduction to AI (artificial intelligence)
The ppt link is available bellow https://docs.google.com/presentation/d/1-oaO75DEdP259HNrrvh5fbZVOtaiiiffi3luyv0tShw/edit?usp=sharing
you could leave your comments on google slides
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
The Internet of Things (IoT) is a term used to describe a network of interconnected devices that are able to communicate with each other and share data. These devices can be anything from smart appliances in your home to sensors in a manufacturing plant or even wearable devices like fitness trackers. The applications of IoT are numerous and continue to expand as technology advances. In this article, we'll explore some of the most important and interesting applications of IoT.
Smart Homes and Buildings
One of the most well-known applications of IoT is in smart homes and buildings. IoT devices can be used to control everything from temperature and lighting to security systems and home entertainment systems. Smart home devices can be controlled remotely through smartphones or other devices, and can even be automated based on the user's preferences and routines.
Smart buildings take this concept a step further, with IoT sensors and systems used to optimize energy usage, monitor air quality, and even control elevators and other building systems. These applications can help reduce energy costs, improve safety, and enhance the overall user experience.
Healthcare
IoT has a variety of applications in the healthcare industry, from wearable devices that monitor vital signs to smart pills that track medication usage. IoT sensors can also be used to monitor patients in hospital settings, allowing medical staff to detect changes in a patient's condition more quickly and respond accordingly.
In addition, IoT devices can be used for remote patient monitoring, allowing patients to receive care in their own homes rather than having to travel to a medical facility. This can improve patient outcomes and reduce healthcare costs.
Agriculture
IoT sensors and systems are increasingly being used in the agricultural industry to optimize crop yields and reduce waste. These sensors can be used to monitor soil moisture, temperature, and nutrient levels, allowing farmers to make more informed decisions about when and how to water and fertilize their crops.
In addition, IoT systems can be used to track the movement and health of livestock, helping farmers to detect and respond to potential health issues more quickly.
Manufacturing
IoT is also being used in the manufacturing industry to improve efficiency and reduce waste. IoT sensors can be used to monitor equipment and machinery, providing real-time data on performance and identifying potential maintenance issues before they become more serious
Businesses across the world are rapidly leveraging the Internet-of-Things (#IoT) to create new products and services that are opening up new business opportunities and creating new business models.
The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story [6].
For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention [6].
Why the Internet of Things needs AI & interoperability to succeedNuance Communications
Gartner predicts 5.5 million new ‘things’ will be connected everyday this year. The challenge: non-intuitive interfaces, disconnected systems, and incompatible APIs mean we’ve not yet been able to unleash the greatest potential of this IoT-connected devices ecosystem. That’s why we need to innovate with interoperability in mind.
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
AIoT(Artificial intelligence of things) is a relatively new term and has recently become a hot topic which combines two of the hottest acronyms AI( Artificial intelligence) and Internet of things (IoT)
An introduction to AI (artificial intelligence)Bellaj Badr
An introduction to AI (artificial intelligence)
The ppt link is available bellow https://docs.google.com/presentation/d/1-oaO75DEdP259HNrrvh5fbZVOtaiiiffi3luyv0tShw/edit?usp=sharing
you could leave your comments on google slides
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
The Internet of Things (IoT) is a term used to describe a network of interconnected devices that are able to communicate with each other and share data. These devices can be anything from smart appliances in your home to sensors in a manufacturing plant or even wearable devices like fitness trackers. The applications of IoT are numerous and continue to expand as technology advances. In this article, we'll explore some of the most important and interesting applications of IoT.
Smart Homes and Buildings
One of the most well-known applications of IoT is in smart homes and buildings. IoT devices can be used to control everything from temperature and lighting to security systems and home entertainment systems. Smart home devices can be controlled remotely through smartphones or other devices, and can even be automated based on the user's preferences and routines.
Smart buildings take this concept a step further, with IoT sensors and systems used to optimize energy usage, monitor air quality, and even control elevators and other building systems. These applications can help reduce energy costs, improve safety, and enhance the overall user experience.
Healthcare
IoT has a variety of applications in the healthcare industry, from wearable devices that monitor vital signs to smart pills that track medication usage. IoT sensors can also be used to monitor patients in hospital settings, allowing medical staff to detect changes in a patient's condition more quickly and respond accordingly.
In addition, IoT devices can be used for remote patient monitoring, allowing patients to receive care in their own homes rather than having to travel to a medical facility. This can improve patient outcomes and reduce healthcare costs.
Agriculture
IoT sensors and systems are increasingly being used in the agricultural industry to optimize crop yields and reduce waste. These sensors can be used to monitor soil moisture, temperature, and nutrient levels, allowing farmers to make more informed decisions about when and how to water and fertilize their crops.
In addition, IoT systems can be used to track the movement and health of livestock, helping farmers to detect and respond to potential health issues more quickly.
Manufacturing
IoT is also being used in the manufacturing industry to improve efficiency and reduce waste. IoT sensors can be used to monitor equipment and machinery, providing real-time data on performance and identifying potential maintenance issues before they become more serious
Businesses across the world are rapidly leveraging the Internet-of-Things (#IoT) to create new products and services that are opening up new business opportunities and creating new business models.
The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story [6].
For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention [6].
Why the Internet of Things needs AI & interoperability to succeedNuance Communications
Gartner predicts 5.5 million new ‘things’ will be connected everyday this year. The challenge: non-intuitive interfaces, disconnected systems, and incompatible APIs mean we’ve not yet been able to unleash the greatest potential of this IoT-connected devices ecosystem. That’s why we need to innovate with interoperability in mind.
oT can give you great insight into consumer behaviour and demand, helping to you create the innovative, revenue-generating services of the future. However, there are still lots of challenges around collecting data from devices, which often have significant limitations in terms of processing power, memory and interfaces.
In this presentation, Danilo talks about how Amazon AI services can be used to augment device capabilities to make data collection, storage and analytics easier. He also considers how people can start interacting with machines in a more natural way, for example using natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS).
Learning objectives:
- Learn how to design IoT solutions using services such as AWS Greengrass and AWS IoT
- Gain insights into practical use cases for Amazon AI services
- Understand the possibilities of using AI from an IoT device
AWS is hosting the first FSI Cloud Symposium in Hong Kong, which will take place on Thursday, March 23, 2017 at Grand Hyatt Hotel. The event will bring together FSI customers, industry professional and AWS experts, to explore how to turn the dream of transformation, innovation and acceleration into reality by exploiting Cloud, Voice to Text and IoT technologies. The packed agenda includes expert sessions on a host of pressing issues, such as security and compliance, as well as customer experience sharing on how cloud computing is benefiting the industry.
Speaker: Timothee Cruse, Global Accounts Solutions Architect, AWS
What Exactly Is The "Internet of Things"?Postscapes
Over the last several years, stories of the technologies making up an Internet of Things have started to slip into public consciousness. As this is occurring, we believe the whole story of Smart Systems and the Internet of Things is not being told. Many of the dispatches coming in from the “front lines” of technology innovation are but fragments of a much larger narrative.
Postscapes collaborated with Harbor Research on an infographic to tell a more complete story about the Internet of Things.
From our perspective, this story is not just about people communicating with people or machines communicating with machines. Smart, connected systems are a technological and economic phenomenon of unprecedented scale, encompassing potentially billions if not trillions of nodes -- an Internet of infinite interactions and values...
IoT Security: Problems, Challenges and SolutionsLiwei Ren任力偉
As a novel computing platform in network, IoT will bring many security challenges to enterprise networks, and create new opportunities for security industry. This talk will provide a general overview of enterprise network security problems, especially the data security, caused by IoT. After that, a few existing security technologies are evaluated as necessary elements of a holistic network security that cover IoT devices. These technologies include : (a) IoT security monitoring and control; (b) FOTA for firmware vulnerability management; (c) NetFlow based big data security analysis. In the end, the practice of standard security protocols (such as OpenIoC and IODEF) will be strongly advocated for delivering effective IoT security solutions.
To obtain a foundational understanding of how the Internet of Things applies to your business, begin by exploring the answers to five key questions. To learn more, check out our special Internet of Things section in Deloitte Review Issue 17: http://deloi.tt/1TwfcmI
Security in the Internet Of Things.
Every IoT project must be designed with security in mind. Identity Relationship Management is a must for a successful IoT implementation.
Dear Sir/Ma’am
I am interested to work as a data specialist in your organization. I believe my experience, skills and work attitude will aid your organization in a great way. Please accept my enclosed resume with this letter.
I worked at Accenture for the last four years. My key responsibilities here were to collect, analyse, store and create data. I made sure that these data were accurate and not damaged. As far as my educational background is concerned, I have a bachelor's degree in EXTC. I am excellent at solving problems and have great analytical skills. I am capable of working well with network administration and can explain the technical problems.
I would appreciate if we could meet up for an interview wherein we can discuss more on this. I can be contacted at +919493377607 or you can email me at imtiaz.khan.sw39@gmail.com
Thank You.
Yours sincerely,
Imtiaz Khan
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Spark + AI Summit - The Importance of Model Fairness and Interpretability in ...Francesca Lazzeri, PhD
Machine learning model fairness and interpretability are critical for data scientists, researchers and developers to explain their models and understand the value and accuracy of their findings. Interpretability is also important to debug machine learning models and make informed decisions about how to improve them. In this session, Francesca will go over a few methods and tools that enable you to “unpack" machine learning models, gain insights into how and why they produce specific results, assess your AI systems fairness and mitigate any observed fairness issues.
Using open source fairness and interpretability packages, attendees will learn how to:
- Explain model prediction by generating feature importance values for the entire model and/or individual datapoints.
- Achieve model interpretability on real-world datasets at scale, during training and inference.
- Use an interactive visualization dashboard to discover patterns in data and explanations at training time.
- Leverage additional interactive visualizations to assess which groups of users might be negatively impacted by a model and compare multiple models in terms of their fairness and performance.
How to Make Cars Smarter: A Step Towards Self-Driving CarsVMware Tanzu
We are moving towards the reality of self-driving cars, but we are still years away from fully autonomous vehicles. In the meantime, however, there are a number of things we can do to make cars smarter in order to improve the lives of drivers. We can use data and analytics, for example, to prevent breakdowns and predict problems before they occur. Technology can also help cars achieve better performance in extreme situations like hydroplaning.
The reality is that data collected by car sensors is underused today.
In this webinar, we will examine:
How to detect patterns in massive amounts of connected car data
Use cases for connected car applications, such as predicting failure of parts and subsystems before they occur
How to apply analytics in real time to help drivers avoid dangerous situations
How to leverage independent data sources to increase predictive value
Deep Dive: In the second half of the webinar we will give an actual example of how we apply big data technology to this problem.
Recommender Systems from A to Z – The Right DatasetCrossing Minds
In the last years a lot of improvements were done in the field of Machine Learning and the Tools that support the community of developers. But still, implementing a recommender system is very hard.
That is why at Crossing Minds, we decided to create a series of 4 meetups to discuss how to implement a recommender system end-to-end:
Part 1 – The Right Dataset
Part 2 – Model Training
Part 3 – Model Evaluation
Part 4 – Real-Time Deployment
This first meetup will be about building the right dataset and doing all the preprocessing needed to create different models. We will talk about explicit vs implicit feedback, dataset analysis, likes/dislikes vs ratings, users and items features, normalization and similarities.
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
The importance of model fairness and interpretability in AI systemsFrancesca Lazzeri, PhD
Machine learning model fairness and interpretability are critical for data scientists, researchers and developers to explain their models and understand the value and accuracy of their findings. Interpretability is also important to debug machine learning models and make informed decisions about how to improve them.
In this session, Francesca will go over a few methods and tools that enable you to "unpack” machine learning models, gain insights into how and why they produce specific results, assess your AI systems fairness and mitigate any observed fairness issues.
Using open-source fairness and interpretability packages, attendees will learn how to:
- Explain model prediction by generating feature importance values for the entire model and/or individual data points.
- Achieve model interpretability on real-world datasets at scale, during training and inference.
- Use an interactive visualization dashboard to discover patterns in data and explanations at training time.
- Leverage additional interactive visualizations to assess which groups of users might be negatively impacted by a model and compare multiple models in terms of their fairness and performance.
The large O’Reilly survey on serverless adoption indicated that the majority of enterprises have not yet adopted serverless. They have cited the following concerns as main factors: security, the steep learning curve, vendor lock-in, integration/debugging and observability of serverless applications.
In this talk, I will share my views on these concerns and present how Waylay IO has addressed these challenges. Waylay IO’s mission is to finally unlock all promised benefits of serverless computation, with an intuitive and developer-friendly low-code platform.
Solving the weak spots of serverless with directed acyclic graph modelVeselin Pizurica
So far Finite State Machine (AWS Step Functions) and Flow Engines have been used functions orchestration. They both have difficulties in dealing with modelling complex logic, stream merging, async processing, task coordination, state sharing, data dependency etc. In this talk I will present a novel approach to serverless orchestration based on Directed Acyclic Graph model.
How to use probabilistic inference programming for application orchestration ...Veselin Pizurica
As companies are adopting serverless architectures and moving away from monolithic and microservice-based deployments, they realise that the challenge lies not only in the rewrite of an old application, but also in the shift towards a new way of thinking. We see many serverless architecture patterns today, such as function chaining, function chaining with rollback (for transaction), ASync HTTP, fan-out and more. We also have a number of tools on the market that ease application development using serverless, of which Apache OpenWhisk (via action chaining or using function composites) and Amazon Step Functions are some of the more popular. In this talk, we will present a new alternative way of building serverless applications based on the orchestration of typed functions, using the probabilistic inference programing paradigm. Inference-based programming brings about the best of the current modelling approaches: the expressiveness and simplicity of decision trees, the high debugging capabilities of state machines, the scalability and flexibility of flow based programming and superior logic expressions to forward chaining approaches. The talk will include a live demo of how to use probabilistic inference programming for a complex IoT application.
A practical look at how to build & run IoT business logicVeselin Pizurica
Automation is what takes IoT projects further than visualisation dashboards and offline analysis into real-world actions that drive results. Rule engines are automation frameworks that enable companies to accelerate application development and support the complexity and scale that IoT automation requires.
We will have a practical look at how you can evaluate any rules engine by immediately matching your unique business logic requirements with the necessary rules engine capabilities.
Google Cloud infrastructure in Conrad Connect by Google & waylayVeselin Pizurica
Conrad Connect lets users interconnect smart devices from different ecosystems with online services. It provides customized dashboards to visualise data from different vendors. It also allows users to build advanced automation rules or to control devices and services using voice and smart bots.
Conrad Connect application is built on top of the waylay platform and it is managed and deployed in the Google cloud.
With close to 100K connected devices, 20 million API calls a day and few billion metrics per week stored, many challenges need to be addressed: How to constantly scale up the platform with exponential growth of the users? How to manage deployments, new releases and upgrades?
In this talk you will learn more how waylay leverages some of the latest Google technologies to address these challenges
Automation, intelligence and knowledge modellingVeselin Pizurica
Automation, intelligence and knowledge modelling,
My talk at http://web11.org/
Numerous talks, news articles and blog posts have been written about impact of recent advances in technology to our society. To a layman, it is all mix of "good news/bad news" show: from improvements in transport, agriculture or health, to jobs disappearing, or wealth inequality, just to name a few. But to techies like myself, the real question is somehow different: How far we can go?.
My talk on webRTC from June 2013
Demo application using XMPP for signalling
open source webRTC using websockets is here: implenentationhttps://github.com/pizuricv/webRTC-over-websockets
A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applic...Veselin Pizurica
The First International Conference on Cognitive Internet of Things Technologies
Talk: A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applications
Authors: Veselin Pizurica, Piet Vandaele
Company: waylay
Website: http://coiot.org/2014/show/program-final
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
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DevOps and Testing slides at DASA ConnectKari Kakkonen
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
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UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Artificial intelligence and IoT
1. Programmable web of the future
{
firstName: Veselin,
lastName: Pizurica,
epochTime: 1381953702
}
Free Powerpoint Templates
Page 1
2. Today talk is about the future future of the web
Integration/convergence:
– API’s
– Sensor Networks/M2M
– Cloud
– Data mining
– Intelligent decision engines
Page 2
3. Introduction to AI
– Learning, Pattern recognition
– Intelligent agents
– Probabilistic reasoning and uncertainty
– Graphical models
Page 3
4. Material used
•
•
•
•
UGent AI course: http://telin.ugent.be/~sanja/ArtificialIntelligence
BaysiaLab
white
paper
Wikipedia
Google
search
Page 4
5. Map
of
Analy;c
Modeling
Page
Breiman
(2001)
and
Shmueli
(2010) 5
8. Intelligent
agents
Agent:
an
en;ty
that
perceives
and
acts
(from
La;n
agere,
to
do)
Ra)onal
agent
is
one
that
acts
so
as
to
achieve
the
best
outcome,
or
when
there
is
uncertainty,
the
best
expected
outcome
Abstractly,
an
agent
is
a
func;on
from
percept
histories
to
ac;ons:
For
any
given
class
of
environments
and
tasks,
we
seek
the
agent
(or
class
of
agents)
with
the
best
performance
In
prac;ce,
computa;onal
limita;ons
make
perfect
ra;onality
unachievable
à
design
best
program
for
given
machine
resources
Page 8
8
9. Ra;onality
• A
ra;onal
agent
is
one
that
does
the
right
thing.
• How
do
we
know
whether
it
is
the
right
thing?
-‐
By
considering
the
consequences
of
the
agent
behavior
(i.e.,
the
sequence
of
states
through
which
the
environment
goes
as
a
result
of
agent’s
behavior)
• A
sequence
of
states
(through
which
the
environment
goes)
is
evaluated
by
a
performance
measure
Page 9
9
10. Specifying
the
task
environment
To design a rational agent, we must specify the
task environment
Consider the task of designing an automated taxi:
– Performance measure: safety, destination, profits, legality,
comfort
– Environment: streets/freeways, traffic, pedestrians, weather
– Actuators: steering, accelerator, brake, horn, speaker/display
– Sensors: video, acceleromaters, gauges, engine sensors,
keyboard, sensors
Page 10
10
19. Agent
types
• Four
basic
types
in
order
of
increasing
generality:
– simple
reflex
agents
– reflex
agents
with
state
– goal-‐based
agents
– u;lity-‐based
agents
All
these
can
be
turned
into
learning
agents
Page 19
19
24. Why
learning?
Why
do
we
want
an
agent
to
learn?
(Why
not
program
an
improved
design
from
the
beginning)?
– Cannot
an;cipate
all
possible
situa;ons
that
the
agent
might
find
itself
in
– Cannot
an;cipate
all
changes
over
;me
– Programmers
might
not
know
how
to
program
a
solu;on
themselves
(e.g.
how
to
program
face
recogni;on)
Learning
modifies
the
agent's
decision
mechanisms
to
improve
performance
Page 24
25. Paaern
recogni;on
Unsupervised
learning
– Learning
paaerns
without
explicit
feedback
supplied
– The
system
forms
clusters
or
natural
groupings
of
the
input
paaerns
(based
on
some
similarity
criteria).
➡Clustering
Reinforcement
learning
– Learning
from
a
series
of
reinforcements
–
rewards
and
punishments
Supervised
learning
– Learning
a
func;on
that
maps
input
to
output
based
on
available
(observed)
input-‐output
pairs
(Correct
answers
for
each
instance)
Semi-‐supervised
learning
– A
few
labeled
samples
available
and
a
large
collec;on
of
unlabeled
ones
– Learn
from
geometry
of
unlabeled
samples
and
use
the
labeled
ones
Page 25
to
improve
the
learning
27. Unsupervised Learning
•
•
No labeled training sets are provided
System applies a specified clustering/grouping criteria to unlabeled dataset Clusters/groups
together “most similar” objects (according to given criteria)
Page 27
28. Pattern Recognition Process
Data acquisition and sensing
– Measurements of physical variables.
– Important issues: bandwidth, resolution , etc.
Pre-processing
– Removal of noise in data.
– Isolation of patterns of interest from the
background.
Feature extraction
– Finding a new representation in terms of
features.
Classification
– Using features and learned models to assign a
pattern to a category.
Post-processing
– Evaluation of confidence in decisions.
Page 28
29. Feature vectors
Single object represented by several features, e.g. shape, size, color,
weight
x1 = shape(e.g.nr of sides)
x2 = size(e.g. some numeric value)
x3 = color (e. g. rgb values)
xd = some other(numeric)feature.
X becomes a feature vector
Page 29
34. PCA
Principal component analysis (PCA) is
a orthogonal transformation to convert
a set of observations of possibly
correlated variables into a set of
values of linearly uncorrelated
variables called principal components.
It is not, however, optimized for class
separability. An alternative is the
linear discriminant analysis, which
does take this into account. PCA is
also sensitive to the scaling of the
variables.
Page 34
35. Deep Learning
• Choosing the correct feature representation of input
data, is a way that people can bring prior knowledge of a
domain to increase an algorithm's computational
performance and accuracy. To move towards general
artificial intelligence, algorithms need to be less
dependent on this feature engineering and better learn to
identify the explanatory factors of input data on their
own.
• Deep learning tries to move in this direction by capturing
a 'good' representation of input data by using
compositions of non-linear transformations.
Page 35
36. Two types of models
• Probabilistic graphical models have
nodes in each layer that are considered
as latent random variables. In this case,
you care about the probability
distribution of the input data x and the
hidden latent random variables h that
describe the input data in the joint
distribution p(x,h). These latent random
variables describe a distribution over the
observed data.
• Direct encoding (neural network) models
have nodes in each layer that are
considered as computational units. This
means each node h performs some
computation (normally nonlinear like a
sigmoidal function) given its inputs from
the previous layer.
Page 36
37. Decision trees
1. Learn rules from data
2. Apply each rule at each
node
3. Classification is at the
leafs of the tree
Page 37
38. Decision Trees example
Example:
decision
whether
to
wait
for
a
table
in
a
restaurant
depending
on
the
following
aaributes:
1. Alternate
(Alt):
Is
there
a
suitable
alterna;ve
restaurant
nearby?
2.
Bar:
Is
there
a
comfortable
bar
area
in
the
restaurant,
where
I
can
wait?
3.
Fri/Sat
(Fri):
True
on
Fridays/Saturdays
4.
Hungry
(Hun):
Are
we
hungry?
5.
Patrons
(Pat):
How
many
people
are
in
the
restaurant
(None,
Some
or
Full)
6.
Price:
the
restaurant’s
price
range
($,
$$,
$$$)
7.
Raining
(Rain):
Is
it
raining
outside?
8.
ReservaBon
(Res):
Did
we
make
a
reserva;on?
9.
Type:
the
kind
of
restaurant
(French,
Italian,
Thai
or
burger)
10.
WaitEsBmate
(Est):
the
wait
;me
es;mated
by
the
host
(0-‐10min,
10-‐30,
30-‐60,
or
>60)
Page 38
39. Decision tree
How
many
dis;nct
decision
trees
we
have
with
n
Boolean
aaributes?
=
number
of
Boolean
func;on
=
number
of
dis;nct
truth
tables
with
2^n
rows
=
2^n^n
E.g., with 6 Boolean attributes 18,446,744,073,709,551,616
Page 39
40. Uncertainty
Let
At
denote
the
ac;on
“leave
for
airport
t
minutes
before
flight”
Will
At
get
me
there
on
;me?
?
?
?
• Purely
logical
approach
leads
to
weak
conclusions:
§
“A90
will
get
me
there
on
;me
if
there
is
no
accident
on
the
way
and
it
doesn't
rain
and
my
;res
remain
intact
and
no
meteorite
hits
the
car,
etc”
§ None
of
these
can
be
inferred
for
sure
à
plan
success
cannot
be
inferred
Page 40
40
41. Uncertainty
• Consider
diagnosis
of
a
pa;ent
with
headache.
Many
reasons
are
possible
like
sinus
problems
or
eye
vision,
tense
muscles,
flu,
cancer,…
Suppose
a
logical
rule
that
aaempts
to
express
this
Headache
⇒
SinusiBs
∨
EyeSight
∨
SBffNeck
∨
Flu
∨
Cancer…
• The
problem
is
that
there
is
almost
unlimited
list
of
possible
causes.
The
causal
rule,
like
SBffNeck=>Headache
doesn’t
work
either
(s;ff
neck
doesn’t
always
cause
headache)
• Trying
to
use
logic
in
this
type
of
domains
fails
because
§ there
is
too
much
work
to
list
all
the
aaributes
§ no
complete
theory
or
knowledge
§ not
all
the
necessary
tests
can
be
or
have
been
run
Page 41
41
42. Why
probabilis;c
reasoning?
• Probabilis;c
reasoning
is
useful
because
logic
olen
fails
due
to
Laziness
and
Ignorance
too
many
aaributes
to
list
Theore;cal
Prac;cal
(no
complete
knowledge
of
the
domain)
(not
enough
observa;ons,
tests,..)
• Probabilis;c
asser;ons
summarize
the
effects
of
laziness
and
ignorance
Page 42
42
44. Graphical
models
Graphical
models
Bayesian
networks
Graphical
models
are
related
to
mathema;cal
graph
theory
Page 44
44
45. Probabilis;c
graphs
• A
graph
is
a
set
of
objects
(represented
by
nodes,
also
called
ver)ces
or
points),
where
some
pairs
of
the
nodes
are
connected
by
links
(edges).
• If
the
edges
are
directed,
they
are
also
called
arrows
and
the
graph
is
directed.
In
a
weighted
graph,
weights
are
assigned
to
the
edges.
The
graph
is
complete
if
all
the
ver;ces
are
connected
to
each
other.
• Probabilis;c
graphs
– nodes
↔
random
variables
(r.v.s)
Page
45
– edges
↔
probabilis;c
dependencies
between
these
r.v.s.
45
46. Common
graphical
models
• Bayesian
networks
–
directed
graphical
models
X
Causal
influence
descendants
of
X
• Markov
random
fields
–
not
directed
graphs
X
neighbors
of
X
Page 46
46
47. Markov
rule
• In
a
directed
graph
P(Xi | all nondescend ants) = P(Xi | Parents(Xi ))
• A
special
case:
Markov
chain
P(Xi | Xi−1,..., X1 ) = P(Xi | Xi−1 )
…
• Markov
random
field
P(Xi | all other nodes) = P(Xi | Neighbors (Xi ))
Page 47
47
48. Markov
Random
Fields
(MRFs)
• Non-‐directed
probabilis;c
graphs
• Used
a
lot
in
digital
image
processing
and
computer
vision
• This
example
illustrates
applica;on
in
image
segmenta;on
Page 48
48
50. Bayes’
rule
Product
rule
P(a ∧ b) = P(a | b) P(b)
P (b | a ) P ( a )
Bayes’
rule
P( a | b) =
P ( b)
Or
in
distribu;on
form
P( X | Y )P(Y )
P(Y | X ) =
=
P(
|
Y
)P(Y )
α
X
P( X )
Useful
for
accessing
diagnos)c
probability
from
causal
probability
P( Effect | Cause)P(Cause)
P(Cause | Effect ) =
P( Effect )
Olen
we
perceive
as
evidence
the
effect
of
some
unknown
cause
and
we
want
to
determine
that
cause,
e,g.
the
chance
of
diseasex
given
symptomy:
P( symptom y | disease x ) P(disease x )
P(disease x | symptom y ) =
P( symptom y )
Page 50
50
51. Bayesian
networks
A
simple,
graphical
nota;on
for
condi;onal
independence
asser;ons
and
hence
for
compact
specifica;on
of
full
joint
distribu;ons
Syntax:
• a
set
of
nodes,
one
per
variable
• a
directed,
acyclic
graph
(each
link
means
“directly
influences”)
• a
condi;onal
distribu;on
for
each
node
given
its
parents:
P( X i | Parents ( X i ))
Page 51
51
52. Network:
directed
acyclic
graph
Descendants
of
X
Non-‐descendants
of
X
Y
edges:
causal
influence
X
nodes:
random
variables
X has causal influence on Y
• Evidence for X forms causal support for Y
• Evidence for Y forms diagnostic support for X
Page 52
52
53. Network
separa;on
Let
us
inves;gate
(condi;onal)
independence
in
three
simple
networks
featuring
these
types
of
nodes,
and
let
denote
“a
and
b
are
condi;onally
independent
given
c”
P(a, b, c) = P(a) P(c | a) P(b | c)
Consider
now
evidence
in
c:
P(a, b) = ∑ P(a) P(c | a ) P(b | c) = P(a) P(b | a )
⇒
c
≠ P(a) P(b)
(in
this
network
a
and
b
are
in
general
not
independent)
P(a, b, c) P(a)P(c | a)P(b | c)
=
=
P(c)
P(c)
= P(a | c)P(b | c)
P(a, b | c) =
So,
we
can
say
that
the
node
c
blocks
the
path
between
a
and
b.
Page 53
53
54. D-‐separa;on
contd.
A,
B
and
C
are
non-‐overlapping
sets
A
C
B
The
sets
A
and
B
are
d-‐separated
by
C
if
each
node
in
A
is
d-‐separated
from
each
node
in
B
by
C
Page 54
54
55. Example:
Car
diagnosis
Ini;al
evidence:
car
won't
start
Testable
variables
(green),
“broken,
so
fix
it”
variables
(orange)
Hidden
variables
(gray)
ensure
sparse
structure,
reduce
parameters
Page 55
55
56. Belief
propaga;on
• Belief
propaga;on
algorithm
was
introduced
by
Judea
Pearl,
1982
• Exact
inference
in
networks
without
loops;
complexity
linear
in
the
number
of
nodes
•
Became
very
popular
aler
it
was
shown
that
the
same
computa;ons
are
in
turbo
codes
and
the
same
principles
in
the
Viterbi
algorithm
• Main
idea:
inference
by
local
message
passing
among
neighboring
nodes
The
message
can
loosely
be
interpreted
as
“I
(node
i )
think
that
you
(node
j)
are
that
much
likely
to
be
in
a
given
state”.
Page 56
56
57. Message
passing
revisited
1.
Distributed
soldier
coun;ng.
2.
Distributed
soldier
coun;ng
with
the
leader
in
line.
Page 57
57
58. Numenta: HTM model
An HTM network consists of regions
arranged in a hierarchy.
Jeff Hawkins: “It combines and
extends approaches used in
Bayesian networks, spatial and
temporal clustering algorithms, while
using a tree-shaped hierarchy of
nodes that is common in neural
networks.”
Read a book, it is a great fun ->
Page 58
59. Semantic web and IBM’s Watson
The "heart and soul” is Unstructured Information Management Architecture [UIMA]
Page 59
60. Presentation 2nd part
• Smart web
– API economy
– IOT
• Bayesian nets
– Troubleshooting and diagnostic
– Sensor integration via plugin framework
– Inteligent decisions and actions
– Cloud deployment
– IFTTT like application using framework above
Page 60
62. API
• APIs have become new patents
• Who holds the data, holds the knowledge
• Companies don’t share their know-how, but
they are willing to share their know-what
(via application programming interface API)
• API economy is coming, and it will be the
major driver of the profit for many
companies
Page 62
66. Sensor Networks
• Network of specialized sensors intended
to monitor and record conditions at diverse
locations.
• Commonly monitored parameters are
temperature, humidity, pressure, wind
direction and speed, illumination intensity,
vibration intensity, sound intensity, powerline voltage, chemical concentrations,
pollutant levels and vital body functions.
Page 66
68. M2M is becoming a reality
API economy has become reality
Page 68
69. Programmable web of the future
Sensors gather and push data to the cloud.
API economies share data and services in the
cloud.
In the cloud, intelligent engine aggregates and
correlates data from different sources,
creating a new VALUE. That can be used
either to:
– Provide new insights (analysis)
– Create new instructions (actions) via API
Page 69
70. Three types of AI/IOT
implementations
• “Ambient intelligence” – mash networks,
information flow and decisions stay local
• “IOT Analytics” – big data like use case
scenarios
• IOT Analytics + API’s + cloud + decision
engine + actions
Page 70
75. Technology that can deal with huge data
sets under complexity and uncertainty?
Page 75
Google/Toyota/Renault/Volvo driverless car research projects
78. Bayesian network modeling
Data analysis technique ideally suited to
messy, complex data. The focus is on
structure discovery – determining an optimal
graphical model which describes the interrelationships in the underlying processes
structure discovery AND inter-relationships
Page 78
79. • How do you express that car needs both
battery and fuel to function? Easy.
• How do you say that if your lights are not
working, most likely it is a battery fault, but
it could be as well that just lights are
broken? Still the fact that lights are not
working point to most likely cause of the
battery fault.
If you only model via composition and add behavior
separately – what most of the tools do these days – you
are heading for complexity!
Page 79
80. Example, car model
Car model with relations: NO Data available
Chance that the car will start is above 98%
Page 80
81. Car example, lights are off
off
Lights are off
Chance that battery functions dropped from 99,99% to less 50%
Chance that the car will start is bellow 50%
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82. Car example, lights are on
on
Lights are on
Battery works, there is no need to check it
Chance that the car will start now only depends on the fuel
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83. Prototype architecture
Database of recipes
Website where
User configures
Logic (recipes)
Decision engine
Pluggable
Actions
Developer extensions (new capabilities)
Pluggable sensors
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